{"id":"https://openalex.org/W2057547934","doi":"https://doi.org/10.1145/1281192.1281297","title":"High-quantile modeling for customer wallet estimation and other applications","display_name":"High-quantile modeling for customer wallet estimation and other applications","publication_year":2007,"publication_date":"2007-08-12","ids":{"openalex":"https://openalex.org/W2057547934","doi":"https://doi.org/10.1145/1281192.1281297","mag":"2057547934"},"language":"en","primary_location":{"id":"doi:10.1145/1281192.1281297","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1281192.1281297","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003651471","display_name":"Claudia Perlich","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Claudia Perlich","raw_affiliation_strings":["IBM T.J. Watson Research Center, Yorktown Heights, NY","IBM -- T. J. Watson Research Center, Yorktown Heights, NY"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM T.J. Watson Research Center, Yorktown Heights, NY","institution_ids":["https://openalex.org/I4210114115"]},{"raw_affiliation_string":"IBM -- T. J. Watson Research Center, Yorktown Heights, NY","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039021124","display_name":"Saharon Rosset","orcid":"https://orcid.org/0000-0002-4458-9545"},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Saharon Rosset","raw_affiliation_strings":["IBM T.J. Watson Research Center, Yorktown Heights, NY","IBM -- T. J. Watson Research Center, Yorktown Heights, NY"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM T.J. Watson Research Center, Yorktown Heights, NY","institution_ids":["https://openalex.org/I4210114115"]},{"raw_affiliation_string":"IBM -- T. J. Watson Research Center, Yorktown Heights, NY","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111487759","display_name":"Richard D. Lawrence","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Richard D. Lawrence","raw_affiliation_strings":["IBM T.J. Watson Research Center, Yorktown Heights, NY","IBM -- T. J. Watson Research Center, Yorktown Heights, NY"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM T.J. Watson Research Center, Yorktown Heights, NY","institution_ids":["https://openalex.org/I4210114115"]},{"raw_affiliation_string":"IBM -- T. J. Watson Research Center, Yorktown Heights, NY","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006347550","display_name":"Bianca Zadrozny","orcid":"https://orcid.org/0000-0002-7260-2057"},"institutions":[{"id":"https://openalex.org/I161127581","display_name":"Universidade Federal Fluminense","ror":"https://ror.org/02rjhbb08","country_code":"BR","type":"education","lineage":["https://openalex.org/I161127581"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Bianca Zadrozny","raw_affiliation_strings":["Universidade Federal Fluminense, Niter\u00f3i, RJ, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade Federal Fluminense, Niter\u00f3i, RJ, Brazil","institution_ids":["https://openalex.org/I161127581"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.527,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.83065937,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"977","last_page":"985"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/quantile","display_name":"Quantile","score":0.8447748422622681},{"id":"https://openalex.org/keywords/quantile-regression","display_name":"Quantile regression","score":0.7470225691795349},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7310510873794556},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.6537132263183594},{"id":"https://openalex.org/keywords/ibm","display_name":"IBM","score":0.6063099503517151},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.5194967985153198},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.5133437514305115},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.49848103523254395},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.41076067090034485},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40156176686286926},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.29073578119277954},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.27075058221817017},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16177284717559814},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.11621522903442383}],"concepts":[{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.8447748422622681},{"id":"https://openalex.org/C63817138","wikidata":"https://www.wikidata.org/wiki/Q3455889","display_name":"Quantile regression","level":2,"score":0.7470225691795349},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7310510873794556},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6537132263183594},{"id":"https://openalex.org/C70388272","wikidata":"https://www.wikidata.org/wiki/Q5968558","display_name":"IBM","level":2,"score":0.6063099503517151},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.5194967985153198},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5133437514305115},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.49848103523254395},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.41076067090034485},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40156176686286926},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29073578119277954},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27075058221817017},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16177284717559814},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.11621522903442383},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C171250308","wikidata":"https://www.wikidata.org/wiki/Q11468","display_name":"Nanotechnology","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1281192.1281297","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1281192.1281297","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.6899999976158142}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W148291325","https://openalex.org/W1481566577","https://openalex.org/W1503303035","https://openalex.org/W1573647811","https://openalex.org/W1943106404","https://openalex.org/W1980054641","https://openalex.org/W1990351156","https://openalex.org/W1994389961","https://openalex.org/W2024046085","https://openalex.org/W2084812512","https://openalex.org/W2096904991","https://openalex.org/W2152350392","https://openalex.org/W2167250202","https://openalex.org/W2175659677","https://openalex.org/W2184776450","https://openalex.org/W2330820318","https://openalex.org/W2912934387","https://openalex.org/W2982720039","https://openalex.org/W3000577762","https://openalex.org/W3085162807","https://openalex.org/W3124622930","https://openalex.org/W6629981006","https://openalex.org/W6671611538","https://openalex.org/W6843735874"],"related_works":["https://openalex.org/W4206511378","https://openalex.org/W4206618949","https://openalex.org/W2526321210","https://openalex.org/W3205863630","https://openalex.org/W4318833145","https://openalex.org/W2364275385","https://openalex.org/W4388704167","https://openalex.org/W2007977664","https://openalex.org/W4376874882","https://openalex.org/W2224749288"],"abstract_inverted_index":{"In":[0],"this":[1,25],"paper":[2],"we":[3,27],"discuss":[4,93],"the":[5,39,44,48,52,76,94,144,161],"important":[6],"practical":[7],"problem":[8,158],"of":[9,15,38,43,55,65,97,134,159,165],"customer":[10],"wallet":[11,66,82],"estimation,":[12],"i.e.,":[13],"estimation":[14,83,151],"potential":[16],"spending":[17],"by":[18],"customers(rather":[19],"than":[20,47],"their":[21],"expected":[22],"spending).":[23],"For":[24],"purpose":[26],"utilize":[28],"quantile":[29,37,72,129,140,150],"modeling,":[30,130],"whose":[31],"goal":[32,54],"is":[33,51],"to":[34,139],"estimate":[35],"a":[36,63,81],"discriminative":[40],"conditional":[41],"distribution":[42],"response,":[45],"rather":[46],"mean,":[49],"which":[50],"implicit":[53],"most":[56],"standard":[57],"regression":[58],"approaches.":[59],"We":[60,91,123,142],"argue":[61],"that":[62],"notion":[64],"can":[67,102],"be":[68,103],"captured":[69],"through":[70],"high":[71,149],"modeling":[73,101],"(e.g,":[74],"estimating":[75,106,160],"90th":[77],"percentile),":[78],"and":[79,110,118,121,131,136],"describe":[80],"implementation":[84],"within":[85],"IBM's":[86],"Market":[87],"Alignment":[88],"Program":[89],"(MAP).":[90],"also":[92],"wide":[95],"range":[96],"domains":[98],"where":[99],"high-quantile":[100],"practically":[104],"important:":[105],"opportunities":[107],"in":[108,148,152],"sales":[109],"marketing":[111],"domains,":[112,154],"defining":[113],"'surprising'":[114],"patterns":[115],"for":[116,128],"outlier":[117],"fraud":[119],"detection":[120],"more.":[122],"survey":[124],"some":[125],"existing":[126],"approaches":[127,138],"propose":[132],"adaptations":[133],"nearest-neighbor":[135],"regression-tree":[137],"modeling.":[141],"demonstrate":[143],"various":[145],"models'":[146],"performance":[147],"several":[153],"including":[155],"our":[156],"motivating":[157],"'realistic'":[162],"IT":[163],"wallets":[164],"IBM":[166],"customers.":[167]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2015,"cited_by_count":4},{"year":2014,"cited_by_count":1},{"year":2012,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
